Job Description

About the Team: Our teams build the intelligence layer that powers personalization, relevance, and product understanding across Uber Eats. Every search result, carousel, recommendation, and product detail page is touched by our systems. From grocery staples to restaurant dishes, we make it easier for users to find the right items at the right time. From an ML perspective, our scope is broad and impactful: Personalization & Recommendations : We design models that learn from user preferences and contexts to surface the most relevant grocery items and restaurant dishes, driving both discovery and loyalty. Catalog Understanding & Enrichment : We build models that identify what each item truly is-brand, flavor, attributes, and the customer segments it appeals to-turning raw merchant data into a clean and intuitive shopping experience. Product Relationships : Our systems learn how items relate to one another-what's a substitute, what's often bought together, and what combinations optimize outcomes for both customers and merchants. Inventory Forecasting : Grocery inventory is volatile and high-stakes. We develop large-scale forecasting systems to predict availability and reduce substitutions-operating at a scale few companies reach. Computer Vision for Real-World Mapping : We use cutting-edge computer vision to digitize physical grocery stores in real time, enabling accurate inventory systems and powering operational excellence. We're tackling some of the most complex and high-impact problems in personalization and catalog ML-at Uber scale. This is a high-visibility team with a mandate to redefine how millions of people discover and shop for food every day. Minimum qualifications: PhD or equivalent in Computer Science, Engineering, Mathematics or related field AND 4-years full-time Software Engineering work experience OR 10-years full-time Software Engineering work experience, WHICH INCLUDES 4-years total technical software engineering experience in one or more of the following areas: Note the 4-years total of specialized software engineering experience may have been gained through education and full-time work experience, additional training, coursework, research, or similar (OR some combination of these). The years of specialized experience are not necessarily in addition to the years of Education & full-time work experience indicated. Programming language (e.g. C, C++, Java, Python, or Go) Large-scale training using data structures and algorithms Modern machine learning algorithms (e.g., tree-based techniques, supervised, deep, or probabilistic learning) Machine Learning Software such as Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib 4+ years of people management experience Technical skills: Required: Deep Learning Scalable ML architecture Experience in applying machine learning models to solve large-scale real-world problems Preferred: Personalization, user understanding and targeting Optimization (RL/Bayes/Bandits) Causal inference For New York, NY-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 per year. For San Francisco, CA-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 per year. For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. You will also be eligible for various benefits. More details can be found at the following link Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form- #J-18808-Ljbffr Uber

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